A System to Improve the Accuracy of Numeric Weather Prediction (NWP) for Flood Forecasting Systems

Data provided by EPS (Ensemble Prediction Systems) are crucial for Flood Forecasting Systems (FFS). In fact, most of known FFS such as those with hydraulic models give flooding alerts thanks to raw data provided by weather predictions. However, frequent change of atmosphere behavior due to anthropic factors may alter the forecast of precipitation as well as the temperature variation. Moreover, existing FFS rely entirely on EPS raw data without any pretreatment that aims to face inaccuracy of weather predictions. As a consequence, it is almost impossible to get the precise flood predictions enough earlier to allow authorities or populations taking the special cares. Bearing this in mind, it is primordial to improve the quality of data obtained from EPS in order to increase the accuracy of FFS. The goal of this paper is to propose an extension of a FFS by introducing a correction module that use real-time data collected from sensor networks combined with past and forecasted data of EPS. The results obtained from empiric experiments show the benefits of our correction mechanism in flood predictions.